{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0074676",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np # linear algebra\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "#from sklearn.datasets import load_boston\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "import shap\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "import math\n",
    "from datetime import datetime, date \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "100d3964",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요한 패키지 import\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "#import xgboost as xgb\n",
    "from sklearn.inspection import permutation_importance\n",
    "from skopt import BayesSearchCV\n",
    "from sklearn.metrics import make_scorer, mean_squared_error, r2_score\n",
    "from sklearn.inspection import partial_dependence, PartialDependenceDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1cd40e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>class</th>\n",
       "      <th>month</th>\n",
       "      <th>flow</th>\n",
       "      <th>sed_obs</th>\n",
       "      <th>prec</th>\n",
       "      <th>tmin</th>\n",
       "      <th>sce</th>\n",
       "      <th>logq</th>\n",
       "      <th>logsl</th>\n",
       "      <th>...</th>\n",
       "      <th>rf_100dnn45_cum_wrp95</th>\n",
       "      <th>rf_1dnn85_annual</th>\n",
       "      <th>rf_1dnn85_annual_cum</th>\n",
       "      <th>rf_100dnn85_cum_wrp5</th>\n",
       "      <th>rf_100dnn85_cum_wrp50</th>\n",
       "      <th>rf_100dnn85_cum_wrp95</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "      <th>year.1</th>\n",
       "      <th>Mass retained sediment (MT) MDPI water paper</th>\n",
       "      <th>gross storage loss MAF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1991-01-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>479.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-19.725</td>\n",
       "      <td>67.8</td>\n",
       "      <td>2.680336</td>\n",
       "      <td>1.832509</td>\n",
       "      <td>...</td>\n",
       "      <td>347.345841</td>\n",
       "      <td>171.80118</td>\n",
       "      <td>171.80118</td>\n",
       "      <td>125.422086</td>\n",
       "      <td>194.165845</td>\n",
       "      <td>364.692377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985</td>\n",
       "      <td>135.825093</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  class  month   flow  sed_obs  prec    tmin   sce      logq   \n",
       "0  1991-01-07    0.0      1  479.0     68.0   0.0 -19.725  67.8  2.680336  \\\n",
       "\n",
       "      logsl  ...  rf_100dnn45_cum_wrp95  rf_1dnn85_annual   \n",
       "0  1.832509  ...             347.345841         171.80118  \\\n",
       "\n",
       "   rf_1dnn85_annual_cum  rf_100dnn85_cum_wrp5  rf_100dnn85_cum_wrp50   \n",
       "0             171.80118            125.422086             194.165845  \\\n",
       "\n",
       "   rf_100dnn85_cum_wrp95  Unnamed: 32  year.1   \n",
       "0             364.692377          NaN    1985  \\\n",
       "\n",
       "   Mass retained sediment (MT) MDPI water paper  gross storage loss MAF  \n",
       "0                                    135.825093                   0.079  \n",
       "\n",
       "[1 rows x 36 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"D:/research/sediment/bisham_sed_all.csv\")\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "181a37c1",
   "metadata": {},
   "source": [
    "# Split Time series Train and Test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "052e5593",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df.sort_values(by='date', inplace=True)\n",
    "\n",
    "start = pd.to_datetime('1991-01-01')  # End date of training data\n",
    "end = pd.to_datetime('2014-12-31')   # End date of test data\n",
    "\n",
    "# Divide the data\n",
    "df1 = df[(df['date'] >= start) & (df['date'] <= end)]\n",
    "#print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4441940c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        flow   prec    tmin\n",
      "498   1342.7  0.000  -2.575\n",
      "499   1143.4  0.000  -4.616\n",
      "500    940.0  0.028  -7.295\n",
      "501    922.9  0.000  -7.043\n",
      "502    959.8  6.818  -4.992\n",
      "...      ...    ...     ...\n",
      "1988   637.2  0.000 -10.672\n",
      "1989   576.2  0.000  -9.206\n",
      "1990   552.8  0.666 -13.026\n",
      "1991   531.5  0.000 -12.975\n",
      "1992   503.5  0.000 -15.919\n",
      "\n",
      "[1495 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Calculate the index for the split\n",
    "split_index = int(0.25 * len(df1))\n",
    "\n",
    "# Divide the data\n",
    "train_data = df1[split_index:]\n",
    "test_data = df1[:split_index]\n",
    "\n",
    "# Define features (x) and target variable (y)\n",
    "x = df1[['flow','prec','tmin']]  # Feature(s),'prec','tmin'\n",
    "y = df1['sed_obs']  # Target variable\n",
    "\n",
    "# Split train data into x_train and y_train\n",
    "x_train = train_data[['flow','prec','tmin']]  # Feature(s) for training\n",
    "y_train = train_data['sed_obs']  # Target variable for training\n",
    "\n",
    "# Split test data into x_test and y_test\n",
    "x_test = test_data[['flow','prec','tmin']]  # Feature(s) for testing\n",
    "y_test = test_data['sed_obs']\n",
    "print(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e003ff13",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "459b0e51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best R-squared Score:  0.5516149210454164\n",
      "Best parameters found:  OrderedDict([('max_depth', 64), ('min_samples_leaf', 63), ('min_samples_split', 56), ('n_estimators', 12), ('random_state', 21)])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# RF-Regressor \n",
    "rf_model = RandomForestRegressor(random_state=42)  #by defualt bootstrapping (with replacement is on) and max_feature = auto\n",
    "\n",
    "# Hyperparameter optimization\n",
    "param_space = {\n",
    "    'n_estimators': (10,300),          #number of trees in the forest\n",
    "    'random_state':(10,100),           \n",
    "    'max_depth': (1, 100),             #maximum depth of the decision trees\n",
    "    'min_samples_split': (2, 100),     #minimum number of samples required to split an internal node\n",
    "    'min_samples_leaf': (2, 100),      #minimum no of samples in a leaf node\n",
    "}\n",
    "\n",
    "#Bayeasionsearch CV\n",
    "opt = BayesSearchCV(\n",
    "    rf_model,\n",
    "    param_space,\n",
    "    #scoring=make_scorer(mean_squared_error, greater_is_better=False),\n",
    "    #scoring='neg_mean_squared_error', \n",
    "    scoring='r2',\n",
    "    n_jobs=-1,\n",
    "    n_iter=50,  #the number of trying optimization\n",
    "    cv=5  \n",
    ")\n",
    "\n",
    "opt.fit(x_train, y_train)\n",
    "\n",
    "print(\"Best R-squared Score: \", opt.best_score_)\n",
    "# Evaluate on the test data\n",
    "\n",
    "print(\"Best parameters found: \", opt.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "22076457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(ccp_alpha=0.01, max_depth=10, max_features=&#x27;sqrt&#x27;,\n",
       "                      min_samples_leaf=2, min_samples_split=10,\n",
       "                      random_state=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(ccp_alpha=0.01, max_depth=10, max_features=&#x27;sqrt&#x27;,\n",
       "                      min_samples_leaf=2, min_samples_split=10,\n",
       "                      random_state=50)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(ccp_alpha=0.01, max_depth=10, max_features='sqrt',\n",
       "                      min_samples_leaf=2, min_samples_split=10,\n",
       "                      random_state=50)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c9e10887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test R-squared:  0.5813654832435706\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = opt.best_estimator_.predict(x_test)   #estimator_\n",
    "test_r2 = r2_score(y_test, y_test_pred)\n",
    "print(\"Test R-squared: \", test_r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "31358784",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract R-squared scores from the BayesSearchCV results from KFOLD CV we have 5 fold 4for traing and 1 for test\n",
    "iterations = range(1, len(opt.cv_results_['mean_test_score']) + 1) # this result is from 1 fold from 5 during optimization\n",
    "r2_scores = opt.cv_results_['mean_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e42b0cf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot R-squared scores against iterations\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.plot(iterations, r2_scores, marker='o',markersize=10,linestyle='-',linewidth=3, color='b', label='KFOLD-CV R$^2$ (Mean)')\n",
    "plt.axhline(y=test_r2, color='g', linestyle='--',linewidth=4, label=f'Test R$^2$') #: {test_r2:.2f}\n",
    "\n",
    "# Add plot title and labels\n",
    "plt.title('Q Bayesian search CV 75:25 Train:Test', fontsize=30)\n",
    "plt.xlabel('Iteration', fontsize=30)\n",
    "plt.ylabel('R$^2$', fontsize=36)\n",
    "\n",
    "plt.xticks(fontsize=30)  # Increase x ticks size\n",
    "plt.yticks(fontsize=30)  # Increase y ticks size\n",
    "\n",
    "# Format y-axis ticks in 10's power format\n",
    "#plt.gca().yaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "#plt.gca().ticklabel_format(style=\"sci\", axis=\"y\", scilimits=(0,0))\n",
    "# Get the current tick locations and labels\n",
    "\n",
    "# Increase the size of tick lines\n",
    "plt.gca().tick_params(axis='x', which='major', length=18, width=4)  # Increase the size of x tick lines\n",
    "plt.gca().tick_params(axis='y', which='major', length=18, width=4)  # Increase the size of y tick lines\n",
    "\n",
    "# Add legend\n",
    "legend = plt.legend(loc='upper left', prop={'size': 30}, bbox_to_anchor=(0.02, 0.96), borderaxespad=0.)\n",
    "legend.get_frame().set_linewidth(4)\n",
    "legend.get_frame().set_edgecolor('black')\n",
    "\n",
    "# Increase the thickness of the outer boundary\n",
    "ax = plt.gca()\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_linewidth(4)\n",
    "    \n",
    "ax.set_yticks(np.arange(0.3, 0.7, 0.1))  # This is just an example range for the primary axis\n",
    "ax.set_ylim(0.32, 0.7) \n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa92505c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "451e83a2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ed8f432e",
   "metadata": {},
   "source": [
    "# PDP Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "73f57bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate PDP for 'flow'\n",
    "pdp_flow = partial_dependence(opt, x_train, features=[0], grid_resolution=100)\n",
    "\n",
    "# Generate PDP for 'prec'\n",
    "pdp_prec = partial_dependence(opt, x_train, features=[1], grid_resolution=100)\n",
    "\n",
    "# Generate PDP for 'tmin'\n",
    "pdp_tmin = partial_dependence(opt, x_train, features=[2], grid_resolution=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8e47ee8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))  # Increased figure size\n",
    "\n",
    "# Generate PDP for all three features: flow, precipitation, and tmin\n",
    "display = PartialDependenceDisplay.from_estimator(opt, x_train, features=[0, 1, 2], ax=ax)\n",
    "\n",
    "# List of x-labels for the three features\n",
    "x_labels = ['Flow [$m^3$/s]', 'Precp [mm]', 'Tmin [°C]']\n",
    "\n",
    "# Loop through each axis (subplots) and apply customizations\n",
    "for i, ax in enumerate(display.axes_.ravel()):  # 'axes_' gives you access to all subplots\n",
    "    # Set x-labels for each subplot\n",
    "    ax.set_xlabel(x_labels[i], fontsize=34)\n",
    "    \n",
    "    # Only set the y-label for the first plot\n",
    "    if i == 0:\n",
    "        ax.set_ylabel('SSC [mg/l]', fontsize=34)\n",
    "    else:\n",
    "        ax.set_ylabel('')  # No y-label for other subplots\n",
    "\n",
    "    # Customize tick parameters\n",
    "    ax.tick_params(axis='x', which='major', length=18, width=4, labelsize=30)  # X-axis tick marks\n",
    "    ax.tick_params(axis='y', which='major', length=18, width=4, labelsize=30)  # Y-axis tick marks\n",
    "\n",
    "    # Increase the thickness of the plot boundary\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_linewidth(4)\n",
    "\n",
    "    # Increase the thickness of the plot lines\n",
    "    for line in ax.get_lines():\n",
    "        line.set_linewidth(5)  # Increase line thickness\n",
    "        \n",
    "    # Add only horizontal grid lines\n",
    "    ax.grid(True, axis='y', linestyle='--', linewidth=2)  # Only horizontal grid lines\n",
    "# Add legend\n",
    "legend = plt.legend(loc='upper right', prop={'size': 34}, bbox_to_anchor=(0.99, 0.98), borderaxespad=0., ncol=4)\n",
    "legend.get_frame().set_linewidth(4)\n",
    "legend.get_frame().set_edgecolor('black')\n",
    "\n",
    "# Adjust layout for better spacing\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da4a2331",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4ca3db44",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05349405",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0a6da39f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = opt.best_estimator_.feature_importances_\n",
    "\n",
    "\n",
    "# Create a list of feature names\n",
    "feature_names = ['Flow [$m^3$/s]', 'Precp [mm]', 'Tmin [°C]']\n",
    "\n",
    "# Create a bar plot for feature importance (features on x-axis)\n",
    "plt.figure(figsize=(18, 10))\n",
    "bar_width = 0.4  # Set the desired width for the bars\n",
    "plt.bar(feature_names, importances, color='skyblue', width=bar_width)\n",
    "\n",
    "# Adding titles and labels\n",
    "#plt.title('Feature Importance for Random Forest Model', fontsize=34)\n",
    "#plt.xlabel('Features', fontsize=34)\n",
    "plt.ylabel('Importance', fontsize=34)\n",
    "\n",
    "# Customize ticks\n",
    "plt.xticks(fontsize=30)  # Increase x ticks size\n",
    "plt.yticks(fontsize=30)  # Increase y ticks size\n",
    "\n",
    "# Increase the size of tick lines\n",
    "plt.gca().tick_params(axis='x', which='major', length=18, width=4)  # Increase the size of x tick lines\n",
    "plt.gca().tick_params(axis='y', which='major', length=18, width=4)  # Increase the size of y tick lines\n",
    "\n",
    "plt.axvline(x=1.5, color='black', linestyle='--', linewidth=4)  # For the first division\n",
    "plt.axvline(x=0.5, color='black', linestyle='--', linewidth=4) \n",
    "\n",
    "# Increase the thickness of the plot boundary\n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_linewidth(4)\n",
    "\n",
    "\n",
    "# Display the plot\n",
    "plt.tight_layout()\n",
    "plt.yticks(np.arange(0, 1, 0.2))  \n",
    "plt.ylim(0, 1)\n",
    "plt.xlim(-0.5,2.5)\n",
    "plt.grid(True, linestyle='--', linewidth=2) \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8651da7a",
   "metadata": {},
   "source": [
    "# Permutation Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a0e0279f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate permutation importance\n",
    "perm_importance = permutation_importance(opt, x_test, y_test, n_repeats=100, random_state=42)\n",
    "\n",
    "# Extract the mean importance and standard deviation for each feature\n",
    "perm_means = perm_importance.importances_mean\n",
    "perm_stds = perm_importance.importances_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "89801d48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.10460414 0.00722923 0.00800201]\n"
     ]
    }
   ],
   "source": [
    "result = permutation_importance(opt, x_test, y_test, n_repeats=30, random_state=42)\n",
    "\n",
    "# Extracting importance values\n",
    "importance = result.importances_mean\n",
    "print(importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "64ea6210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a list of feature names\n",
    "feature_names = ['Flow [$m^3$/s]', 'Precp [mm]', 'Tmin [°C]']\n",
    "\n",
    "# Create a bar plot for permutation importance (features on x-axis)\n",
    "plt.figure(figsize=(18, 10))\n",
    "bar_width = 0.4  # Set the desired width for the bars\n",
    "plt.bar(feature_names, perm_means, color='lightcoral', yerr=perm_stds, width=bar_width, capsize=5)\n",
    "\n",
    "# Adding titles and labels\n",
    "plt.ylabel('Permutation Importance', fontsize=34)\n",
    "\n",
    "# Customize ticks\n",
    "plt.xticks(fontsize=30)  # Increase x ticks size\n",
    "plt.yticks(fontsize=30)  # Increase y ticks size\n",
    "\n",
    "# Increase the size of tick lines\n",
    "plt.gca().tick_params(axis='x', which='major', length=18, width=4)  # Increase the size of x tick lines\n",
    "plt.gca().tick_params(axis='y', which='major', length=18, width=4)  # Increase the size of y tick lines\n",
    "\n",
    "# Increase the thickness of the plot boundary\n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_linewidth(4)\n",
    "\n",
    "# Add grid lines\n",
    "plt.grid(True, linestyle='--', linewidth=2)\n",
    "\n",
    "# Adjust layout for better spacing\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "413ee409",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b7d7fb8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a list of feature names\n",
    "feature_names = ['Flow [$m^3$/s]', 'Precp [mm]', 'Tmin [°C]']\n",
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "bar_width = 0.2  # Set the desired width for the bars\n",
    "x_positions = np.arange(len(feature_names))\n",
    "\n",
    "# Create a bar plot for feature importance (shifted left by bar_width/2)\n",
    "plt.bar(x_positions - bar_width/2, importances, color='skyblue', width=bar_width,label='Feature')\n",
    "\n",
    "# Create a bar plot for permutation importance (shifted right by bar_width/2)\n",
    "plt.bar(x_positions + bar_width/2, perm_means, color='lightcoral', yerr=perm_stds,\n",
    "        width=bar_width, capsize=5, label='Permutation')\n",
    "\n",
    "# Adding titles and labels\n",
    "plt.ylabel('Importance', fontsize=38)\n",
    "\n",
    "# Customize x-ticks to match feature names\n",
    "plt.xticks(ticks=x_positions, labels=feature_names, fontsize=38)\n",
    "\n",
    "# Customize y-ticks\n",
    "plt.yticks(fontsize=30)\n",
    "\n",
    "# Increase the size of tick lines\n",
    "plt.gca().tick_params(axis='x', which='major', length=18, width=4)  # Increase the size of x tick lines\n",
    "plt.gca().tick_params(axis='y', which='major', length=18, width=4)  # Increase the size of y tick lines\n",
    "\n",
    "# Increase the thickness of the plot boundary\n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_linewidth(4)\n",
    "\n",
    "    \n",
    "# Add legend\n",
    "legend = plt.legend(loc='upper right', prop={'size': 34}, bbox_to_anchor=(0.99, 0.98), borderaxespad=0., ncol=1)\n",
    "legend.get_frame().set_linewidth(4)\n",
    "legend.get_frame().set_edgecolor('black')\n",
    "\n",
    "# Add grid lines\n",
    "plt.grid(True, linestyle='--', linewidth=2)\n",
    "# Adjust layout for better spacing\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8127966e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d45dd301",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80113480",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "062e7005",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "deep_learning1",
   "language": "python",
   "name": "deep_learning1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
